Embryonic layer 5 pyramidal neurons form earliest recurrent circuits with correlated activity

Layer 5 pyramidal neurons (L5 PNs) are a major cortical output. Their connectivity approximates a recurrent neural network (RNN), with the majority of inputs coming from other L5 PNs. These neurons may underlie anesthesia-induced unconsciousness, by restricting information output from cortex. Therefore, understanding the formation of the L5 PN circuitry, from the time at which L5 PNs first enter cortex, would provide insight into how a biological RNN can be constructed. However, this is technically challenging because, in mice, L5 PNs first enter cortex at embryonic day (E) 13.5, five days before birth. Hence, we developed a method of two-photon imaging from cortical neurons, in living embryos, connected to the dam.

Applying this para-uterine imaging method, we observed a structured, bimodal pattern of increased activity in embryonic L5 PNs. Surprisingly, they showed an early peak of activity only a day after migrating into the nascent cortex, at E14.5, followed by a decrease from E15.5 to E16.5, and then a second increase from E17.5 onwards. Further, the neurons responded to glutamatergic agonists. Performing visually-guided patch clamp recordings in living embryos, we found that embryonic L5 PNs showed tetrodotoxin-sensitive active conductances during both periods of increased activity. Together, these results suggest that embryonic activity is mediated similarly as in the adult.

Intriguingly, activity across pairs of embryonic L5 PNs was correlated greater than expected by chance. This suggests that embryonic L5 PNs form recurrent circuits with other L5 PNs, as early as E14.5. However, these correlations were independent of distance, and did not form waves. Throughout embryonic development, the precise temporal variation in activity in L5 PNs coincided with changes in circuit organization. This suggests that correlated activity serves a specific function. The para-uterine imaging method gives us all-optical access to characterizing this function and, thereby, to understanding this biological RNN’s construction.